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Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 112 (from laksa165)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa112.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'http://www.tonyohagan.co.uk/academic/Reading.html\')), getAhrefsUnparsedNoserviceFromURL(\'http://www.tonyohagan.co.uk/academic/Reading.html\'))) FORMAT JSONEachRow'

Response:

{"found_url":"http:\/\/www.tonyohagan.co.uk\/academic\/Reading.html","crawl_time":1776178347,"first_indexed_time":1531868536,"http_code":200,"src_unparsed":"uk,co,tonyohagan!www,\/academic\/Reading.html h80","src_root_hash":"14827794075291986512","history_drop_reason":null,"meta_title":"Bayesian reading","meta_descriptions":[],"attrs_boilerpipe_text":"A Bayesian Statistics Reading List\nI am frequently asked to recommend books on Bayesian Statistics.  Here are a few\nsuggestions, with notes. Note that this page was written in 2004.  There are surely many\nnewer and excellent books, but I still think the ones listed here are worth studying.\nPreparatory books\nIn this category I include some recommendations that are preparatory reading.\nA very fine book by Dennis Lindley deals with decision making, in very much a Bayesian \nway using personal probability.  And my 1988 book is an introduction to personal \nprobability, which I believe it is important to understand if you are interested in\nlearning about Bayesian Statistics.\nNeither book assumes knowledge of mathematics above a very elementary level.\nLindley, D.V. (1980).\nMaking Decisions\n, 2nd ed. Wiley, New York.\nO'Hagan, A. (1988).\nProbability: Methods and Measurement\n. Chapman and Hall, London.\nIntroductory books\nThe best introductory text at a truly introductory level, with only the most elementary \nmathematics required, is by Don Berry.  It has a slant to medical applications.\nBerry, D.A. (1996).\nStatistics: A Bayesian Perspective\n. Duxbury, London.\nThe best text that is also introductory but assumes college level mathematics is by \nPeter Lee.\nLee, P.M. (2004).\nBayesian Statistics: An Introduction\n, 3rd edition, Arnold, London.\nMore advanced texts\nAt an intermediate level, there is quite a nice, readable but concise book by Helio Migon\nand Dani Gamerman.\nMigon, H.S. and Gamerman, D. (1999).\nStatistical Inference: An Integrated Approach\n.\nArnold, London.\nThe next two books concentrate on how to develop models and computations for the \npractical application of Bayesian methods. The coverage of the two books overlaps somewhat.\nCongdon, P. (2001).\nBayesian Statistical Modelling\n. John Wiley.\nCongdon, P. (2003).\nApplied Bayesian Models\n. John Wiley.\nThe last two books, by Jose Bernardo and Adrian Smith, and by Jon Forster and myself, are for people \nwishing to learn Bayesian Statistics in depth. Both assume good mathematical knowledge\nand some previous introduction to statistical theory.\nBernardo, J.M. and Smith, A.F.M. (1994).\nBayesian Theory\n. Wiley, New York.\nO'Hagan, A. and Forster, J. J. (2004).\nBayesian Inference\n, 2nd edition, \nvolume 2B of \"Kendall's Advanced Theory of Statistics\". Arnold, London.\nUpdated: 11 January 2017\nMaintained by: Tony O'Hagan","attrs_markdown":"[![University of Sheffield](http:\/\/www.tonyohagan.co.uk\/Images\/tuoslogo_rgb_lo.gif)](http:\/\/www.shef.ac.uk\/)\n\nTony O'Hagan - Academic pages\n\n![.](http:\/\/www.tonyohagan.co.uk\/academic\/none)\n***\n# A Bayesian Statistics Reading List\n***\nI am frequently asked to recommend books on Bayesian Statistics. Here are a few suggestions, with notes. Note that this page was written in 2004. There are surely many newer and excellent books, but I still think the ones listed here are worth studying.\n\n### Preparatory books\nIn this category I include some recommendations that are preparatory reading. A very fine book by Dennis Lindley deals with decision making, in very much a Bayesian way using personal probability. And my 1988 book is an introduction to personal probability, which I believe it is important to understand if you are interested in learning about Bayesian Statistics.\n\nNeither book assumes knowledge of mathematics above a very elementary level.\n\n- Lindley, D.V. (1980). *Making Decisions*, 2nd ed. Wiley, New York.\n- O'Hagan, A. (1988). *Probability: Methods and Measurement*. Chapman and Hall, London.\n### Introductory books\nThe best introductory text at a truly introductory level, with only the most elementary mathematics required, is by Don Berry. It has a slant to medical applications.\n\n- Berry, D.A. (1996). *Statistics: A Bayesian Perspective*. Duxbury, London.\n\nThe best text that is also introductory but assumes college level mathematics is by Peter Lee.\n\n- Lee, P.M. (2004). *Bayesian Statistics: An Introduction*, 3rd edition, Arnold, London.\n### More advanced texts\nAt an intermediate level, there is quite a nice, readable but concise book by Helio Migon and Dani Gamerman.\n\n- Migon, H.S. and Gamerman, D. (1999). *Statistical Inference: An Integrated Approach*. Arnold, London.\n\nThe next two books concentrate on how to develop models and computations for the practical application of Bayesian methods. The coverage of the two books overlaps somewhat.\n\n- Congdon, P. (2001). *Bayesian Statistical Modelling*. John Wiley.\n- Congdon, P. (2003). *Applied Bayesian Models*. John Wiley.\n\nThe last two books, by Jose Bernardo and Adrian Smith, and by Jon Forster and myself, are for people wishing to learn Bayesian Statistics in depth. Both assume good mathematical knowledge and some previous introduction to statistical theory.\n\n- Bernardo, J.M. and Smith, A.F.M. (1994). *Bayesian Theory*. Wiley, New York.\n- O'Hagan, A. and Forster, J. J. (2004). *Bayesian Inference*, 2nd edition, volume 2B of \"Kendall's Advanced Theory of Statistics\". Arnold, London.\n***\n|   |   |   |   |   |   |\n|---|---|---|---|---|---|\n| [![]()](http:\/\/www.tonyohagan.co.uk\/) | [![My academic pages](http:\/\/www.tonyohagan.co.uk\/Images\/Academic\/Academic.png)](http:\/\/www.tonyohagan.co.uk\/academic) | [![]()](http:\/\/www.tonyohagan.co.uk\/consult) | [![]()](http:\/\/www.tonyohagan.co.uk\/resources) | [![]()](http:\/\/www.tonyohagan.co.uk\/leisure) | [![]()](http:\/\/www.tonyohagan.co.uk\/contacts.html) |\n***\nUpdated: 11 January 2017\n\nMaintained by: Tony O'Hagan","attrs_readable_markdown":null,"meta_canonical":null}

3. Robots.txt Check

Query:

Response:

4. Spam/Ban Check

Query:

Response:

5. Seen Status Check

ℹ️ Skipped - page is already crawled

📄

INDEXABLE

✅

CRAWLED

16 hours ago

🤖

ROBOTS ALLOWED

Page Info Filters

Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
Age cutoff	PASS	`download_stamp > now() - 6 MONTH`	0 months ago
History drop	PASS	`isNull(history_drop_reason)`	No drop reason
Spam/ban	PASS	`fh_dont_index != 1 AND ml_spam_score = 0`	ml_spam_score=0
Canonical	PASS	`meta_canonical IS NULL OR = '' OR = src_unparsed`	Not set

Page Details

Property	Value
URL	http://www.tonyohagan.co.uk/academic/Reading.html
Last Crawled	2026-04-14 14:52:27 (16 hours ago)
First Indexed	2018-07-17 23:02:16 (7 years ago)
HTTP Status Code	200
Meta Title	Bayesian reading
Meta Description	null
Meta Canonical	null
Boilerpipe Text	A Bayesian Statistics Reading List I am frequently asked to recommend books on Bayesian Statistics. Here are a few suggestions, with notes. Note that this page was written in 2004. There are surely many newer and excellent books, but I still think the ones listed here are worth studying. Preparatory books In this category I include some recommendations that are preparatory reading. A very fine book by Dennis Lindley deals with decision making, in very much a Bayesian way using personal probability. And my 1988 book is an introduction to personal probability, which I believe it is important to understand if you are interested in learning about Bayesian Statistics. Neither book assumes knowledge of mathematics above a very elementary level. Lindley, D.V. (1980). Making Decisions , 2nd ed. Wiley, New York. O'Hagan, A. (1988). Probability: Methods and Measurement . Chapman and Hall, London. Introductory books The best introductory text at a truly introductory level, with only the most elementary mathematics required, is by Don Berry. It has a slant to medical applications. Berry, D.A. (1996). Statistics: A Bayesian Perspective . Duxbury, London. The best text that is also introductory but assumes college level mathematics is by Peter Lee. Lee, P.M. (2004). Bayesian Statistics: An Introduction , 3rd edition, Arnold, London. More advanced texts At an intermediate level, there is quite a nice, readable but concise book by Helio Migon and Dani Gamerman. Migon, H.S. and Gamerman, D. (1999). Statistical Inference: An Integrated Approach . Arnold, London. The next two books concentrate on how to develop models and computations for the practical application of Bayesian methods. The coverage of the two books overlaps somewhat. Congdon, P. (2001). Bayesian Statistical Modelling . John Wiley. Congdon, P. (2003). Applied Bayesian Models . John Wiley. The last two books, by Jose Bernardo and Adrian Smith, and by Jon Forster and myself, are for people wishing to learn Bayesian Statistics in depth. Both assume good mathematical knowledge and some previous introduction to statistical theory. Bernardo, J.M. and Smith, A.F.M. (1994). Bayesian Theory . Wiley, New York. O'Hagan, A. and Forster, J. J. (2004). Bayesian Inference , 2nd edition, volume 2B of "Kendall's Advanced Theory of Statistics". Arnold, London. Updated: 11 January 2017 Maintained by: Tony O'Hagan
Markdown	[![University of Sheffield](http://www.tonyohagan.co.uk/Images/tuoslogo_rgb_lo.gif)](http://www.shef.ac.uk/) Tony O'Hagan - Academic pages ![.](http://www.tonyohagan.co.uk/academic/none) * # A Bayesian Statistics Reading List * I am frequently asked to recommend books on Bayesian Statistics. Here are a few suggestions, with notes. Note that this page was written in 2004. There are surely many newer and excellent books, but I still think the ones listed here are worth studying. ### Preparatory books In this category I include some recommendations that are preparatory reading. A very fine book by Dennis Lindley deals with decision making, in very much a Bayesian way using personal probability. And my 1988 book is an introduction to personal probability, which I believe it is important to understand if you are interested in learning about Bayesian Statistics. Neither book assumes knowledge of mathematics above a very elementary level. - Lindley, D.V. (1980). Making Decisions, 2nd ed. Wiley, New York. - O'Hagan, A. (1988). Probability: Methods and Measurement. Chapman and Hall, London. ### Introductory books The best introductory text at a truly introductory level, with only the most elementary mathematics required, is by Don Berry. It has a slant to medical applications. - Berry, D.A. (1996). Statistics: A Bayesian Perspective. Duxbury, London. The best text that is also introductory but assumes college level mathematics is by Peter Lee. - Lee, P.M. (2004). Bayesian Statistics: An Introduction, 3rd edition, Arnold, London. ### More advanced texts At an intermediate level, there is quite a nice, readable but concise book by Helio Migon and Dani Gamerman. - Migon, H.S. and Gamerman, D. (1999). Statistical Inference: An Integrated Approach. Arnold, London. The next two books concentrate on how to develop models and computations for the practical application of Bayesian methods. The coverage of the two books overlaps somewhat. - Congdon, P. (2001). Bayesian Statistical Modelling. John Wiley. - Congdon, P. (2003). Applied Bayesian Models. John Wiley. The last two books, by Jose Bernardo and Adrian Smith, and by Jon Forster and myself, are for people wishing to learn Bayesian Statistics in depth. Both assume good mathematical knowledge and some previous introduction to statistical theory. - Bernardo, J.M. and Smith, A.F.M. (1994). Bayesian Theory. Wiley, New York. - O'Hagan, A. and Forster, J. J. (2004). Bayesian Inference, 2nd edition, volume 2B of "Kendall's Advanced Theory of Statistics". Arnold, London. * \| \| \| \| \| \| \| \|---\|---\|---\|---\|---\|---\| \| [![]()](http://www.tonyohagan.co.uk/) \| [![My academic pages](http://www.tonyohagan.co.uk/Images/Academic/Academic.png)](http://www.tonyohagan.co.uk/academic) \| [![]()](http://www.tonyohagan.co.uk/consult) \| [![]()](http://www.tonyohagan.co.uk/resources) \| [![]()](http://www.tonyohagan.co.uk/leisure) \| [![]()](http://www.tonyohagan.co.uk/contacts.html) \| * Updated: 11 January 2017 Maintained by: Tony O'Hagan
Readable Markdown	null
Shard	112 (laksa)
Root Hash	14827794075291986512
Unparsed URL	uk,co,tonyohagan!www,/academic/Reading.html h80